title: Likelihood Gradient Ascent (LGA): a closed-loop decoder adaptation algorithm for brain-machine interfaces.
abstract: Closed-loop decoder adaptation (CLDA) is an emerging paradigm for improving or maintaining the online performance of brain-machine interfaces (BMIs). Here, we present Likelihood Gradient Ascent (LGA), a novel CLDA algorithm for a Kalman filter (KF) decoder that uses stochastic, gradient-based corrections to update KF parameters during closed-loop BMI operation. LGA's gradient-based paradigm presents a variety of potential advantages over other "batch" CLDA methods, including the ability to update decoder parameters on any time-scale, even on every decoder iteration. Using a closed-loop BMI simulator, we compare the LGA algorithm to the Adaptive Kalman Filter (AKF), a partially gradient-based CLDA algorithm that has been previously tested in non-human primate experiments. In contrast to the AKF's separate mean-squared error objective functions, LGA's update rules are derived directly from a single log likelihood objective, making it one step towards a potentially optimal continuously adaptive CLDA algorithm for BMIs.
authors.forename: Siddharth
authors.initials: S
authors.lastname: Dangi
authors.forename: Suraj
authors.initials: S
authors.lastname: Gowda
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carmena
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D006801:Humans; D009474:Neurons / Q000502:physiology; D011323:Primates